2020/04/08 by Anna Petrovskaia, Petrovskaia, Anna, Raghavendra B. Jana +3
Computer Science · Engineering · Environmental Science · #Advanced Image Fusion Techniques #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Signal Denoising Methods #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Remote Sensing and LiDAR Applications #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2004.04209
openalex publication_date 2020/04/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Remote sensing images are used for a variety of analyses, from agricultural\nmonitoring, to disaster relief, to resource planning, among others. The images\ncan be corrupted due to a number of reasons, including instrument errors and\nnatural obstacles such as clouds. We present here a novel approach for\nreconstruction of missing information in such cases using only the corrupted\nimage as the input. The Deep Image Prior methodology eliminates the need for a\npre-trained network or an image database. It is shown that the approach easily\nbeats the performance of traditional single-image methods.\n